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Speaking sociologically with big data: symphonic social science and the future for big data research

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Speaking sociologically with big data : symphonic social science and the future for big data research. / Halford, Susan; Savage, Mike.

In: Sociology, Vol. 51, No. 6, 06.2017, p. 1132-1148.

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@article{37fbb772fa884371974bdd91ce57d86a,
title = "Speaking sociologically with big data: symphonic social science and the future for big data research",
abstract = "Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about ‘the social’, and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Putnam (2000), Wilkinson and Pickett (2009) and Piketty (2014) that we label ‘symphonic social science’. This bears both striking similarities and significant differences to the big data paradigm and – as such – offers the potential to do big data analytics differently. This offers value to those already working with big data – for whom the difficulties of making useful and sustainable claims about the social are increasingly apparent – and to sociologists, offering a mode of practice that might shape big data analytics for the future.",
keywords = "big data, computational methods, sociology, symphonic social science, visualisation",
author = "Susan Halford and Mike Savage",
year = "2017",
month = "6",
doi = "10.1177/0038038517698639",
language = "English",
volume = "51",
pages = "1132--1148",
journal = "Sociology",
issn = "0038-0385",
publisher = "SAGE Publications India",
number = "6",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Speaking sociologically with big data

T2 - symphonic social science and the future for big data research

AU - Halford, Susan

AU - Savage, Mike

PY - 2017/6

Y1 - 2017/6

N2 - Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about ‘the social’, and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Putnam (2000), Wilkinson and Pickett (2009) and Piketty (2014) that we label ‘symphonic social science’. This bears both striking similarities and significant differences to the big data paradigm and – as such – offers the potential to do big data analytics differently. This offers value to those already working with big data – for whom the difficulties of making useful and sustainable claims about the social are increasingly apparent – and to sociologists, offering a mode of practice that might shape big data analytics for the future.

AB - Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about ‘the social’, and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Putnam (2000), Wilkinson and Pickett (2009) and Piketty (2014) that we label ‘symphonic social science’. This bears both striking similarities and significant differences to the big data paradigm and – as such – offers the potential to do big data analytics differently. This offers value to those already working with big data – for whom the difficulties of making useful and sustainable claims about the social are increasingly apparent – and to sociologists, offering a mode of practice that might shape big data analytics for the future.

KW - big data

KW - computational methods

KW - sociology

KW - symphonic social science

KW - visualisation

U2 - 10.1177/0038038517698639

DO - 10.1177/0038038517698639

M3 - Article

VL - 51

SP - 1132

EP - 1148

JO - Sociology

JF - Sociology

SN - 0038-0385

IS - 6

ER -